ArticleObesity (Silver Spring, Md.)2026
A CVD Classification Model for Individuals With Obesity: Multi-Ethnic Validation Based on Multiple Metabolic Indicators.
Article in Obesity (Silver Spring, Md.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveThe global rise in obesity, a major risk factor for cardiovascular disease (CVD), contributes substantially to the disease burden. We developed and validated a model optimized for classifying prevalent CVD in individuals with obesity.
methodsIn the training cohort, risk factors were screened by univariate and LASSO regression and then incorporated into a multivariate logistic model to establish the classifier. Based on this model, a nomogram was constructed. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate model performance. Sensitivity was analyzed using a unified BMI standard in the Korea cohort. A web-based dynamic nomogram was also implemented (https://min115.shinyapps.io/CVDrisk/).
resultsThe final nomogram incorporated age, hypertension, diabetes, metabolic score for visceral Fat (METS-VF), serum creatinine, and blood urea nitrogen. In internal validation, the model achieved an AUC of 0.799, with a Hosmer-Lemeshow p value of 0.823. In external validation, the AUC was 0.821, with a Hosmer-Lemeshow p value of 0.083. In both the internal and external validation sets, the model demonstrated good clinical utility.
conclusionsThis study developed and validated a nomogram for the classification of prevalent CVD in individuals with obesity, providing a validated model optimized for the population with obesity.
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